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Evolutionary computation approaches to tip position controller design for a two-link flexible manipulator

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EN
Abstrakty
EN
Controlling multi-link flexible robots is very difficult compared rigid ones due to inter-link coupling, nonlinear dynamics, distributed link flexure and under-actuation. Hence, while designing controllers for such systems the controllers should be equipped with optimal gain parameters. Evolutionary Computing (EC) approaches such as Genetic Algorithm (GA), Bacteria Foraging Optimization (BFO) are popular in achieving global parameter optimizations. In this paper we exploit these EC techniques in achieving optimal PD controller for controlling the tip position of a two-link flexible robot. Performance analysis of the EC tuned PD controllers applied to a two-link flexible robot system has been discussed with number of simulation results.
Rocznik
Strony
269--285
Opis fizyczny
Bibliogr. 10 poz., rys., tab.
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autor
autor
Bibliografia
  • [1] S. Ozcelik and E. Miranda: Output feed-back direct adaptive control for a two-link flexible robot subject to parameter changes. Adaptive Control, Edited by Kwanho You, In-tech Publisher, Croatia, 2009.
  • [2] W-Y. Han, J-W. Han and C-G. Lee: Development of a self-tuning PID controller based on neural network for nonlinear systems. Proc. of the 7th Mediterranean Conf. on Control and Automation (MED99), Haifa, Israel, (1999), 28-30. 1999.
  • [3] B. Subudhi, A.S. Morris: Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulator. Proc. Instn. Mech. Engrs, 217 Part I: J. Systems and Control Engineering, 2003.
  • [4] Y. Yongquan, H. Ying, W. Minghui, Z. Bi and Z. Guokun: Fuzzy Neural PID controller and tuning its weight factors using genetic algorithm based on Werent location crossover. IEEE Int. Conf. on Systems, Man and Cybernetics, (2004).
  • [5] D. B. Fogel: The advantages of evolutionary computation. Natural Selection, Inc..
  • [6] A. De Luca and B. Siciliano: Closed-form dynamic model of planar multi-link lightweight robots. IEEE Trans. on Systems, Man, and Cybernetics, 21(4), (1991), 826-839.
  • [7] D. E. Goldberg: Genetic algorithm in search, optimization and machine learning. Reading. Reading MA Addison-Wesley, 1989.
  • [8] S. S. Ge, T.H. Lee and G. Zhu: Genetic algorithm tuning of Lyapunov-based controllers: An application to a single-link flexible robot system. IEEE Trans. on Industrial Electronics, 43(5), (1996), 567-574.
  • [9] K. M. Passino: Biomimicry of bacterial foraging for distributed optimization and control. IEEE. Control Syst. Mag., (2002), 52-67.
  • [10] S. Das, A. Biswas, S. Dasgupta and A. Abraham: Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. In: Foundations of Computational Intelligence, 3 ser. Studies in Computational Intelligence, A. Abraham, A.E. Hassanien, P. Siarry and A. Engelbrecht, (Eds). Springer Berlin Heidelberg, 203 (2009), 23-55.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0097-0003
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